Identifying EEG Binary Limb Motor Imagery Movements using Thick Data Analytics

September 30, 2020  |  Vol.6, No.9  |  PP. 169-189  | PDF

AUTHORS:

Vikas Trikha, Department of Computer Science, Lakehead University, Thunder Bay, Ontario, Canada

Jinan Fiaidhi, Department of Computer Science, Lakehead University, Thunder Bay, Ontario, Canada

Sabah Mohammed, Department of Computer Science, Lakehead University, Thunder Bay, Ontario, Canada

KEYWORDS:

EEG Brain Waves, EEG Motor Imagery, BCI Applications, Ensemble Learning, Thick Data Analytics

Abstract

Electroencephalography (EEG) is non-invasive technology that is widely used to record brain signals in brain computer interfacing (BCI) systems to control, motor imagery, in which movements signals occurring in limbs can control some services. Researchers have proposed numerous classification schemes of these motor imagery to incorporate it with various neurorehabilitation, neuroprosthetics and gaming applications. However, the existing classification schemes face the performance degradation caused by motor-imagery EEG signals with low signal to noise ratio. The paper’s main objective is to use possible thick data analytics techniques to classify effectively the motor imagery EEG signals. Our attempt start with notable classifiers including Decision Trees, Extra Trees, Naive Bayes, Random Forest and SVM and move later to enhance classifications using variety of ensemble learning techniques including Bagging, Adaboost and Stacking. More techniques has been applied on the results of the ensemble learring to eliminate classification noise and supply more relevant features such as substituting outliers with mean value and exercising band-pass filter and Common Spatial Pattern (CSP). The thick data methods has been validated on a public dataset rendered by BCI competition II dataset III and was found to produce better classification performance metric which included performance metric parameters like accuracy, specificity, sensitivity, precision and recall when confronted with the existing work, thus projecting the usefulness of motor imagery BCI. The analytics is inclusive of Area Under the Curve (AUC) score and Mathews Correlation Coefficient (MCC) score to display an impactful analysis.

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Citations:

APA:
Trikha, V., Fiaidhi, J., Mohammed, S. (2020). Identifying EEG Binary Limb Motor Imagery Movements using Thick Data Analytics. Asia-pacific Journal of Convergent Research Interchange (APJCRI), ISSN: 2508-9080 (Print); 2671-5325 (Online), FuCoS, 6(9), 169-189. doi: 10.47116/apjcri.2020.09.15

MLA:
Trikha, Vikas, et al. “Identifying EEG Binary Limb Motor Imagery Movements using Thick Data Analytics.” Asia-pacific Journal of Convergent Research Interchange, ISSN: 2508-9080 (Print); 2671-5325 (Online), FuCoS, vol. 6, no. 9, 2020, pp. 169-189. APJCRI, http://fucos.or.kr/journal/APJCRI/Articles/v6n9/15.html.

IEEE:
[1] V. Trikha, J. Fiaidhi, S. Mohammed, “Identifying EEG Binary Limb Motor Imagery Movements using Thick Data Analytics.” Asia-pacific Journal of Convergent Research Interchange (APJCRI), ISSN: 2508-9080 (Print); 2671-5325 (Online), FuCoS, vol. 6, no. 9, pp. 169-189, September 2020.

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